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Tools for Assessing Stray Gas Migration: A Case Study in

Pennsylvania

Seth Pelepko, P.G. & Stew Beattie

PADEP: Bureau of Oil and Gas Planning & Program Development

Stray Gas Incidence & Response Forum

July 25, 2012

Cleveland, OH

• Defining Background in Absence of Pre-Drill Samples: A Multiple Regression Model

• Transitioning from a One-Dimensional Analysis to a Three-Dimensional Analysis: Evaluating Data Trends and Building a 3D Site Conceptual Model

• GIS Applications: Topographic Position Index (TPI), Surface Modeling, and the ArcScene Environment

• Lessons Learned and Improvements Moving Forward

Presentation Outline

Dependent Variable: Predrill Methane for Years Three and Four (n = 307) Add %’s

Defining Background in Absence of Pre-Drill Samples: A Multiple Regression Model

Site Size: Background Area: 172 sq. mi. Stray Gas Site: 10.5 sq. mi.

(81.0%)

(9.0%)

(1.0%)

(2.0%)

(6.5%)

(0.5%)

Independent Variables Considered

• Surface Elevation

• Bottomhole Elevation

• Topographic Position Index (TPI)

• Gas Well Distribution

• Gas Show Density (0 to 500 feet and 0 to 1000 feet)

• Depth to Tully

• Longitude

• Latitude

• % Sand (0 to 500 feet and 0 to 1000 feet)

• Sample Date

• Gas well production for first period of reporting

Defining Background in Absence of Pre-Drill Samples: A Multiple Regression Model

Surface Elevation and Bottomhole Elevation (n = 307)

Defining Background in Absence of Pre-Drill Samples: A Multiple Regression Model

Topographic Position Index (TPI) (USGS 10 m DEM) (Weiss, 2001)

Defining Background in Absence of Pre-Drill Samples: A Multiple Regression Model

Gas Well Distribution (n = 245)

Defining Background in Absence of Pre-Drill Samples: A Multiple Regression Model

Gas Show Density (0 to 500 feet, n = 89; and 0 to 1000 feet, n = 122)

Defining Background in Absence of Pre-Drill Samples: A Multiple Regression Model

Depth to Tully Limestone (n = 229)

Defining Background in Absence of Pre-Drill Samples: A Multiple Regression Model

N-S Extent of Water Supplies

N-S Extent of Gas Wells

A (S) A’ (N)

meters

met

ers

Depth to Tully Limestone (n = 229)

Defining Background in Absence of Pre-Drill Samples: A Multiple Regression Model

A (S) A’ (N)

meters

met

ers

?

?

Longitude and Latitude (n = 307)

Defining Background in Absence of Pre-Drill Samples: A Multiple Regression Model

S

W E

N

% Sand (0 to 500, n = 192; and 0 to 1000 feet, n = 192)

Defining Background in Absence of Pre-Drill Samples: A Multiple Regression Model

Model Results: Under-Predicting Methane

Defining Background in Absence of Pre-Drill Samples: A Multiple Regression Model

Model Results (Stepwise Regression)

• Most important variables for predicting dissolved methane are TPI (X1), latitude (X2), and bottomhole elevation (X3)

• Regression equation is:

ln(methane concentration + 1) = -130.923 - 1.192(ln(TPI+300) + 3.323(latitude) - 0.001(bottomhole elevation)

• Model was statistically significant and no assumptions were violated

• Goodness-of-fit was less than desirable – only 36% of variance explained

Where do we go from here?

• Consider only confirmed thermogenic methane

• Consideration of other parameters or models (non-linear)

• Improved quality control/consistency for existing parameters

Conclusion:

• If 36% of variance can be explained with limited time and resources, there seems to be potential with this approach

Defining Background in Absence of Pre-Drill Samples: A Multiple Regression Model

• 1D: Simple linear regression model to examine dissolved- and free-phase methane time-series trends

• 2D: Shallow groundwater quality spatial and temporal trends

• 3D: Integrating gas well construction and MIT data with subsurface structure, water quality trends, and the time dimension

Transitioning from a One-Dimensional Analysis to a Three-Dimensional Analysis: Evaluating Data Trends and Building a 3D Site Model

Affordable and Accessible Tools:

• Topographic Position Index (TPI)

• Interpolation

– Natural Neighbor

– Kriging

• 3D Modeling and ArcScene

GIS Applications

Valley vs. Ridges

• Is there a methodology for exploring variability in the concentration of methane as a function of location, i.e., in valleys versus ridges, and how do we classify these zones?

GIS Applications

Topographic Position Index (TPI)

• The topographic position index reflects the difference in elevation between a focal cell and all cells in the neighborhood

• Geospatial tool developed by Andrew Weiss in 2001

GIS Applications

TPI

• Positive values indicate the cell is higher than its average surroundings while negative values mean it is lower

GIS Applications

Defining a Surface

• Interpolation

– Estimating values within the range of available data

• Extrapolation

– Predicting values of locations outside the range of available data

GIS Applications

Interpolation: Natural Neighbor

• Natural Neighbor interpolates a value based on the closest subset of samples and weights them based on their proportionate area to the value being interpolated (Sibson,1981)

GIS Applications

• Kriging is an advanced, statistically-based procedure that estimates spatial variables and predicts surface trends using a technique in which the surrounding measured values are weighted to derive a predicted value for an unmeasured location (ESRI, 2012)

– Semivariogram: allows examination of spatial relationships between measured points

– Provides Cross-Validation: “how well” the model predicted unknown values

GIS Applications

3D Modeling and the ArcScene Environment

• The 3D environment permits visualization of a complex subsurface geological problem, e.g., stray gas migration

• By converting data to or compiling it in a suitable format it becomes easy to integrate surface and subsurface data to get a more complete picture

GIS Applications

3D Modeling and the ArcScene Environment

• ArcScene is a 3D visualization application that allows you to view GIS data in three dimensions (ESRI 2012)

GIS Applications

Lessons Learned and Improvements Moving Forward

A Proactive Approach and Progressive Thinking Matters • Improvements in data quality and management will facilitate the

construction of robust statistical and geological models • Some well-construction and operational practices are believed to have

exacerbated conditions at residential water supplies considered in the case study

• Data trends for a dynamic compound like methane may better be inferred

by examining monthly or quarterly averages • Passage of new well-construction regulations in February 2011 has been

accompanied by a decrease in the number of stray gas incidents • Multiple discussions and partnerships with research agencies, academia,

and industry are leading to improved operations throughout the Commonwealth of Pennsylvania

Thank You – Questions?

Seth Pelepko, P.G., LPG Well Plugging and Subsurface Activities Division 717.772.2199 (mpelepko@pa.gov) Stew Beattie, GIS/Information Specialist Well Plugging and Subsurface Activities Division 717.772.2199 (stebeattie@pa.gov)